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Optical character recognition

About: Optical character recognition is a research topic. Over the lifetime, 7342 publications have been published within this topic receiving 158193 citations. The topic is also known as: OCR & optical character reader.


Papers
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Proceedings ArticleDOI
14 Oct 2002
TL;DR: An interface to textual information for the visually impaired that uses video, image processing, optical-character-recognition (OCR) and text-to-speech (TTS) is described.
Abstract: We describe the development of an interface to textual information for the visually impaired that uses video, image processing, optical-character-recognition (OCR) and text-to-speech(TTS). The video provides a sequence of low resolution images in which text must be detected, rectified and converted into high resolution rectangular blocks that are capable of being analyzed via off-the-shelf OCR. To achieve this, various problems related to feature detection, mosaicing, auto-focus, zoom, and systems integration were solved in the development of the system, and these are described.

53 citations

Book ChapterDOI
23 Aug 2020
TL;DR: In this paper, the authors focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or optical character recognition (OCR) models.
Abstract: Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the pdf document or on the optical character recognition (ocr) models to extract low-level layout features from the image. However, these methods fail to generalize well because of the absence of meta-features or errors made by the ocr when there is a significant variance in table layouts and text organization. In our work, we focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or ocr.

53 citations

Journal ArticleDOI
TL;DR: An attempt to develop a commercially viable and a robust character recognizer for Telugu texts by designing a recognizer which exploits the inherent characteristics of the Telugu Script by using wavelet multiresolution analysis and a Hopfield -based Dynamic Neural Network.

53 citations

Patent
11 Jan 1999
TL;DR: In this article, the display of the document image may be augmented by displaying a region corresponding to a reference text within the document text in another visually distinctive manner, in a similar manner as in this paper.
Abstract: Document texts are produced by recognizing characters in document images by an Optical Character Recognition (OCR) process. When such a document text matches one or more search terms of a query, the corresponding document image is displayed. Regions of the document image, corresponding to words of the document text that match the search terms, are displayed in a visually distinctive manner. The display of the document image may be augmented by displaying a region corresponding to a reference text within the document text in another visually distinctive manner.

53 citations

Proceedings ArticleDOI
01 Jan 2000
TL;DR: Two complementary methods are proposed for characterizing the spatial structure of digitized technical documents and labelling various logical components without using optical character recognition.
Abstract: Two complementary methods are proposed for characterizing the spatial structure of digitized technical documents and labelling various logical components without using optical character recognition The top-down method segments and labels the page image simultaneously using publication-specific information in the form of a page-grammar The bottom-up method naively segments the document into rectangles that contain individual connected components, combines blocks using knowledge about generic layout objects, and identifies logical objects using publication-specific knowledge Both methods are based on the X-Y tree representation of a page image The procedures are demonstrated on scanned and synthesized bit-maps of the title pages of technical articles

53 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023186
2022425
2021333
2020448
2019430
2018357